Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Qiang Cheng; Jale Tezcan; Jie Cheng

doi:10.1016/j.patrec.2013.12.010

Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Qiang Cheng, Jale Tezcan, Jie Cheng

Internal Medicine

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

Original language	English
Pages (from-to)	88-95
Number of pages	8
Journal	Pattern Recognition Letters
Volume	40
Issue number	1
DOIs	https://doi.org/10.1016/j.patrec.2013.12.010
State	Published - Apr 15 2014

Bibliographical note

Funding Information:
This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .

Keywords

Confidence interval
Least squares support vector machine
Mixed effect modeling
Prediction interval
Semiparametric function estimation

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patrec.2013.12.010

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title = "Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine",

abstract = "We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.",

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author = "Qiang Cheng and Jale Tezcan and Jie Cheng",

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T1 - Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

AU - Cheng, Qiang

AU - Tezcan, Jale

AU - Cheng, Jie

N1 - Funding Information: This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .

PY - 2014/4/15

Y1 - 2014/4/15

N2 - We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

AB - We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

KW - Confidence interval

KW - Least squares support vector machine

KW - Mixed effect modeling

KW - Prediction interval

KW - Semiparametric function estimation

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Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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